AIPI 510: Predictive Analytics with Wake County Restaurant Inspections

Christine Park, Shyamal Anadkat, Hearsch Jariwala

Client(s): Environmental Health & Safety Division, Wake County

Agenda¶

  1. Business Understanding
  2. Data Understanding
  3. Data Sourcing & Preparation
  4. Modeling
  5. Evaluation
  6. Insights
  7. Q&A

Wake County – Environmental Health & Safety Division¶

"Environmental Health & Safety promotes public health through plan review, permitting and inspections of food service establishments, child day care facilities, nursing homes, hotels, public pools and tattoo artists."

- Wake County Government Website 

The Challenge¶

Background¶

Problem

YoY increase in restaurant inspection violations* as well as critical violations in Wake County. This presents a threat to public health & food safety.

Opportunity

With limited/fixed resources & inspectors, Wake County will benefit from a solution to flag inspections with a higher risk of critical violations.

Many of those violations, include “critical” ones. Critical violations can include storing raw meat near ready-to-eat vegetables, inadequate hand washing or keeping foods at unsafe temperature, among other examples.

Success Metrics¶

  1. Reduction in critical violations year-over-year at Wake County restaurants

  2. Improvement in allocated resources with prioritization of restaurants at higher risk of critical violations

  3. Ultimately, reduction in reported cases of food-borne illnesses

Sourcing Our Data¶

Wake County Restaurants from Wake Gov Open Data¶

Out[64]:
HSISID NAME ADDRESS1 CITY POSTALCODE PHONENUMBER RESTAURANTOPENDATE PERMITID X Y
0 4092016155 DAILY PLANET CAFE 11 W JONES ST RALEIGH 27601 1.919708e+10 2012-04-12 2 -78.639431 35.782205
1 4092016161 HIBACHI 88 3416 POOLE RD RALEIGH 27610 1.919231e+10 2012-04-18 4 -78.579533 35.767246
2 4092017180 BOND BROTHERS BEER COMPANY 202 E CEDAR ST CARY 27511 1.919459e+10 2016-03-11 5 -78.778021 35.787986

Restaurant Inspections Data (2016 - Present) from Wake Gov Open Data¶

Out[63]:
OBJECTID HSISID SCORE DATE TYPE INSPECTOR
0 22332274 4092017542 94.5 2017-04-07 Inspection Anne-Kathrin Bartoli
1 22332275 4092017542 92.0 2017-11-08 Inspection Laura McNeill
2 22332276 4092017542 95.0 2018-03-23 Inspection Laura McNeill

Violations Data (2016 - Present) from Wake Gov Open Data¶

Out[62]:
OBJECTID HSISID INSPECTDATE CATEGORY CRITICAL SEVERITY SHORTDESC INSPECTEDBY POINTVALUE OBSERVATIONTYPE VIOLATIONTYPE
0 191104519 4092012065 2016-06-07 Approved Source No NaN Food obtained from approved source Johanna Hill 0.0 OUT VR
1 191104520 4092017322 2020-07-10 Approved Source No NaN Food obtained from approved source Lauren Harden 0.0 OUT NaN
2 191104521 4092030492 2021-06-14 Approved Source No NaN Food obtained from approved source David Adcock 1.0 OUT NaN

Weather Data (Avg hourly temperature for the day, 2016 - Present) from NOAA¶

Out[65]:
date TAVG
0 2016-01-01 49.0
1 2016-01-02 43.0
2 2016-01-03 40.0

Yelp Data (Yelp Fusion API - Search by term/phone)¶

Out[66]:
name review_count rating price phone display_phone category_title
0 Peace China 63 3.5 1 19196769968 (919) 676-9968 chinese
1 Asian Cafe 7 3.0 2 19196769968 (919) 676-9968 chinese
2 Northside Bistro & Cocktails 23 4.5 -1 19198905225 (919) 890-5225 american (new)
3 The Daily Planet Cafe 89 4.0 2 19197078060 (919) 707-8060 cafes
4 Hibachi 88 46 3.5 1 19192311688 (919) 231-1688 japanese

Daily Police Incidents as proxy for Crime from Wake Gov Open Data¶

Out[67]:
OBJECTID crime_category crime_code crime_description city reported_date reported_year reported_month reported_day reported_dayofwk
0 12001 MISCELLANEOUS 81H Miscellaneous/Missing Person (18 & over) RALEIGH 2017-01-15 2017 1 14 Saturday
1 12002 MISCELLANEOUS 81A Miscellaneous/All Other Non-Offenses RALEIGH 2017-07-29 2017 7 29 Saturday
2 12003 MISCELLANEOUS 81F Miscellaneous/Mental Commitment RALEIGH 2016-03-07 2016 3 6 Sunday

Understanding our data¶

Out[70]:

EDA & Profiling¶

In [12]:
ProfileReport(final_features_df, title="Feauture Profiling Report", explorative=True)
Out[12]:




















Understanding the features¶
















    
    







                            
                    




















Modeling & Evaluation¶
















    
    







                            
                    










    
    






F1 Score: 0.6722129783693843
Accuracy: 0.7149059334298119


























    
    



























Legal, Regulatory and Ethical Considerations¶
    

  1. Project Selection and Scoping
    2. 

  2. Building the team
    3. 

  3. Data Collection
    4. 

  4. Analysis/Modeling
    5. 

  5. Implementation
    6. 












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